Overview

Dataset statistics

Number of variables41
Number of observations2239
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory719.6 KiB
Average record size in memory329.1 B

Variable types

Numeric21
Categorical19
DateTime1

Alerts

Z_CostContact has constant value ""Constant
Z_Revenue has constant value ""Constant
Year_Birth is highly overall correlated with Age and 1 other fieldsHigh correlation
Income is highly overall correlated with Wines and 12 other fieldsHigh correlation
Wines is highly overall correlated with Income and 11 other fieldsHigh correlation
Fruits is highly overall correlated with Income and 9 other fieldsHigh correlation
Meat is highly overall correlated with Income and 10 other fieldsHigh correlation
Fish is highly overall correlated with Income and 9 other fieldsHigh correlation
Sweet is highly overall correlated with Income and 9 other fieldsHigh correlation
Gold is highly overall correlated with Income and 10 other fieldsHigh correlation
Web is highly overall correlated with Income and 7 other fieldsHigh correlation
Catalog is highly overall correlated with Income and 11 other fieldsHigh correlation
Store is highly overall correlated with Income and 10 other fieldsHigh correlation
Web Visit is highly overall correlated with Income and 1 other fieldsHigh correlation
Age is highly overall correlated with Year_Birth and 1 other fieldsHigh correlation
Promotion accepted is highly overall correlated with PromoAccepted and 7 other fieldsHigh correlation
PromoAccepted is highly overall correlated with Promotion accepted and 7 other fieldsHigh correlation
Frequency is highly overall correlated with Income and 10 other fieldsHigh correlation
Monetary is highly overall correlated with Income and 11 other fieldsHigh correlation
Education is highly overall correlated with Education_cleanHigh correlation
Marital_Status is highly overall correlated with Marital_cleanHigh correlation
Kidhome is highly overall correlated with Children and 1 other fieldsHigh correlation
Teenhome is highly overall correlated with Children and 1 other fieldsHigh correlation
AcceptedCmp3 is highly overall correlated with Promotion accepted and 1 other fieldsHigh correlation
AcceptedCmp4 is highly overall correlated with Promotion accepted and 2 other fieldsHigh correlation
AcceptedCmp5 is highly overall correlated with Income and 5 other fieldsHigh correlation
AcceptedCmp1 is highly overall correlated with Promotion accepted and 2 other fieldsHigh correlation
AcceptedCmp2 is highly overall correlated with Promotion accepted and 1 other fieldsHigh correlation
Response is highly overall correlated with Promotion accepted and 2 other fieldsHigh correlation
Age group is highly overall correlated with Year_Birth and 1 other fieldsHigh correlation
Education_clean is highly overall correlated with EducationHigh correlation
Marital_clean is highly overall correlated with Marital_StatusHigh correlation
Children is highly overall correlated with Kidhome and 2 other fieldsHigh correlation
Family_size is highly overall correlated with Kidhome and 2 other fieldsHigh correlation
Responder is highly overall correlated with Promotion accepted and 5 other fieldsHigh correlation
AcceptedCmp3 is highly imbalanced (62.4%)Imbalance
AcceptedCmp4 is highly imbalanced (61.7%)Imbalance
AcceptedCmp5 is highly imbalanced (62.4%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.3%)Imbalance
ID has unique valuesUnique
Recency has 28 (1.3%) zerosZeros
Fruits has 400 (17.9%) zerosZeros
Fish has 384 (17.2%) zerosZeros
Sweet has 419 (18.7%) zerosZeros
Gold has 61 (2.7%) zerosZeros
Deals has 46 (2.1%) zerosZeros
Web has 49 (2.2%) zerosZeros
Catalog has 586 (26.2%) zerosZeros
Promotion accepted has 1630 (72.8%) zerosZeros
PromoAccepted has 1630 (72.8%) zerosZeros

Reproduction

Analysis started2023-07-31 10:53:37.883698
Analysis finished2023-07-31 10:57:03.124913
Duration3 minutes and 25.24 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct2239
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5590.4448
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:03.782689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile576.7
Q12827.5
median5455
Q38423.5
95-th percentile10675.1
Maximum11191
Range11191
Interquartile range (IQR)5596

Descriptive statistics

Standard deviation3246.3725
Coefficient of variation (CV)0.58070021
Kurtosis-1.1893616
Mean5590.4448
Median Absolute Deviation (MAD)2786
Skewness0.040706532
Sum12517006
Variance10538934
MonotonicityNot monotonic
2023-07-31T17:57:04.778507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5524 1
 
< 0.1%
10770 1
 
< 0.1%
5763 1
 
< 0.1%
3478 1
 
< 0.1%
7494 1
 
< 0.1%
1763 1
 
< 0.1%
7250 1
 
< 0.1%
2005 1
 
< 0.1%
6885 1
 
< 0.1%
3584 1
 
< 0.1%
Other values (2229) 2229
99.6%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Year_Birth
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8995
Minimum1940
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:05.598257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1940
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.695504
Coefficient of variation (CV)0.0059401221
Kurtosis-0.79313601
Mean1968.8995
Median Absolute Deviation (MAD)9
Skewness-0.092932825
Sum4408366
Variance136.7848
MonotonicityNot monotonic
2023-07-31T17:57:07.183639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 87
 
3.9%
1975 83
 
3.7%
1970 80
 
3.6%
1972 79
 
3.5%
1978 77
 
3.4%
1965 74
 
3.3%
1973 74
 
3.3%
1969 71
 
3.2%
1974 69
 
3.1%
Other values (46) 1456
65.0%
ValueCountFrequency (%)
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 7
 
0.3%
1944 7
 
0.3%
1945 8
 
0.4%
1946 16
0.7%
1947 16
0.7%
1948 21
0.9%
1949 30
1.3%
1950 29
1.3%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 30
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Graduation
1126 
PhD
486 
Master
370 
2n Cycle
203 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5176418
Min length3

Characters and Unicode

Total characters16832
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1126
50.3%
PhD 486
21.7%
Master 370
 
16.5%
2n Cycle 203
 
9.1%
Basic 54
 
2.4%

Length

2023-07-31T17:57:08.763323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:09.572557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1126
46.1%
phd 486
19.9%
master 370
 
15.2%
2n 203
 
8.3%
cycle 203
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2676
15.9%
r 1496
8.9%
t 1496
8.9%
n 1329
 
7.9%
i 1180
 
7.0%
G 1126
 
6.7%
d 1126
 
6.7%
u 1126
 
6.7%
o 1126
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13701
81.4%
Uppercase Letter 2725
 
16.2%
Decimal Number 203
 
1.2%
Space Separator 203
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2676
19.5%
r 1496
10.9%
t 1496
10.9%
n 1329
9.7%
i 1180
8.6%
d 1126
8.2%
u 1126
8.2%
o 1126
8.2%
e 573
 
4.2%
h 486
 
3.5%
Other values (4) 1087
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 1126
41.3%
D 486
17.8%
P 486
17.8%
M 370
 
13.6%
C 203
 
7.4%
B 54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 203
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16426
97.6%
Common 406
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2676
16.3%
r 1496
9.1%
t 1496
9.1%
n 1329
8.1%
i 1180
 
7.2%
G 1126
 
6.9%
d 1126
 
6.9%
u 1126
 
6.9%
o 1126
 
6.9%
e 573
 
3.5%
Other values (10) 3172
19.3%
Common
ValueCountFrequency (%)
2 203
50.0%
203
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2676
15.9%
r 1496
8.9%
t 1496
8.9%
n 1329
 
7.9%
i 1180
 
7.0%
G 1126
 
6.7%
d 1126
 
6.7%
u 1126
 
6.7%
o 1126
 
6.7%
e 573
 
3.4%
Other values (12) 3578
21.3%

Marital_Status
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Married
864 
Together
579 
Single
480 
Divorced
232 
Widow
 
77
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0728004
Min length4

Characters and Unicode

Total characters15836
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowTogether
4th rowTogether
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 864
38.6%
Together 579
25.9%
Single 480
21.4%
Divorced 232
 
10.4%
Widow 77
 
3.4%
Alone 3
 
0.1%
Absurd 2
 
0.1%
YOLO 2
 
0.1%

Length

2023-07-31T17:57:09.940925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:10.346308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
married 864
38.6%
together 579
25.9%
single 480
21.4%
divorced 232
 
10.4%
widow 77
 
3.4%
alone 3
 
0.1%
absurd 2
 
0.1%
yolo 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2737
17.3%
r 2541
16.0%
i 1653
10.4%
d 1175
7.4%
g 1059
 
6.7%
o 891
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 579
 
3.7%
t 579
 
3.7%
Other values (16) 2894
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13591
85.8%
Uppercase Letter 2245
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2737
20.1%
r 2541
18.7%
i 1653
12.2%
d 1175
8.6%
g 1059
 
7.8%
o 891
 
6.6%
a 864
 
6.4%
t 579
 
4.3%
h 579
 
4.3%
n 483
 
3.6%
Other values (7) 1030
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M 864
38.5%
T 579
25.8%
S 480
21.4%
D 232
 
10.3%
W 77
 
3.4%
A 5
 
0.2%
O 4
 
0.2%
Y 2
 
0.1%
L 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 15836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2737
17.3%
r 2541
16.0%
i 1653
10.4%
d 1175
7.4%
g 1059
 
6.7%
o 891
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 579
 
3.7%
t 579
 
3.7%
Other values (16) 2894
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2737
17.3%
r 2541
16.0%
i 1653
10.4%
d 1175
7.4%
g 1059
 
6.7%
o 891
 
5.6%
M 864
 
5.5%
a 864
 
5.5%
T 579
 
3.7%
t 579
 
3.7%
Other values (16) 2894
18.3%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct1974
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51963.555
Minimum1730
Maximum162397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:10.712534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile19095.1
Q135533.5
median51381.5
Q368277.5
95-th percentile83893.6
Maximum162397
Range160667
Interquartile range (IQR)32744

Descriptive statistics

Standard deviation21410.672
Coefficient of variation (CV)0.41203248
Kurtosis0.75413519
Mean51963.555
Median Absolute Deviation (MAD)16404.5
Skewness0.35010392
Sum1.163464 × 108
Variance4.5841688 × 108
MonotonicityNot monotonic
2023-07-31T17:57:11.065115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51381.5 24
 
1.1%
7500 12
 
0.5%
35860 4
 
0.2%
37760 3
 
0.1%
80134 3
 
0.1%
46098 3
 
0.1%
48432 3
 
0.1%
39922 3
 
0.1%
63841 3
 
0.1%
67445 3
 
0.1%
Other values (1964) 2178
97.3%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%
102692 1
< 0.1%

Kidhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
1293 
1
898 
2
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Length

2023-07-31T17:57:11.462232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:11.744220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1293
57.7%
1 898
40.1%
2 48
 
2.1%

Teenhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
1157 
1
1030 
2
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%

Length

2023-07-31T17:57:12.052986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:12.389243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1157
51.7%
1 1030
46.0%
2 52
 
2.3%
Distinct663
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Minimum2012-07-30 00:00:00
Maximum2014-06-29 00:00:00
2023-07-31T17:57:13.374410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:57:13.696051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.121036
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:14.085798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.963662
Coefficient of variation (CV)0.58963867
Kurtosis-1.2016846
Mean49.121036
Median Absolute Deviation (MAD)25
Skewness-0.0028686102
Sum109982
Variance838.89374
MonotonicityNot monotonic
2023-07-31T17:57:14.425037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.3%
49 30
 
1.3%
65 30
 
1.3%
3 29
 
1.3%
29 29
 
1.3%
71 29
 
1.3%
Other values (90) 1930
86.2%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 27
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 25
1.1%
95 19
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.3%
91 18
0.8%
90 20
0.9%

Wines
Real number (ℝ)

HIGH CORRELATION 

Distinct776
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.06744
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:14.803267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median174
Q3504.5
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)480.5

Descriptive statistics

Standard deviation336.61483
Coefficient of variation (CV)1.10704
Kurtosis0.59750126
Mean304.06744
Median Absolute Deviation (MAD)165
Skewness1.1752394
Sum680807
Variance113309.54
MonotonicityNot monotonic
2023-07-31T17:57:15.334945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 40
 
1.8%
1 37
 
1.7%
6 37
 
1.7%
4 33
 
1.5%
8 30
 
1.3%
3 30
 
1.3%
9 27
 
1.2%
12 25
 
1.1%
10 24
 
1.1%
Other values (766) 1914
85.5%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.3%
4 33
1.5%
5 40
1.8%
6 37
1.7%
7 22
1.0%
8 30
1.3%
9 27
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

Fruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.307727
Minimum0
Maximum199
Zeros400
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:15.934340image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile123
Maximum199
Range199
Interquartile range (IQR)32

Descriptive statistics

Standard deviation39.781468
Coefficient of variation (CV)1.5121591
Kurtosis4.0472677
Mean26.307727
Median Absolute Deviation (MAD)8
Skewness2.1013279
Sum58903
Variance1582.5652
MonotonicityNot monotonic
2023-07-31T17:57:16.390568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
17.9%
1 162
 
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
7 67
 
3.0%
5 65
 
2.9%
6 62
 
2.8%
12 50
 
2.2%
8 48
 
2.1%
Other values (148) 1045
46.7%
ValueCountFrequency (%)
0 400
17.9%
1 162
7.2%
2 120
 
5.4%
3 116
 
5.2%
4 104
 
4.6%
5 65
 
2.9%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.1%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

Meat
Real number (ℝ)

HIGH CORRELATION 

Distinct558
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.01653
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:16.922430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median67
Q3232
95-th percentile687.2
Maximum1725
Range1725
Interquartile range (IQR)216

Descriptive statistics

Standard deviation225.74383
Coefficient of variation (CV)1.3516257
Kurtosis5.5136976
Mean167.01653
Median Absolute Deviation (MAD)59
Skewness2.0826203
Sum373950
Variance50960.276
MonotonicityNot monotonic
2023-07-31T17:57:17.502756image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.2%
11 49
 
2.2%
8 46
 
2.1%
6 43
 
1.9%
10 40
 
1.8%
3 40
 
1.8%
9 38
 
1.7%
16 36
 
1.6%
12 35
 
1.6%
Other values (548) 1809
80.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.3%
3 40
1.8%
4 30
1.3%
5 50
2.2%
6 43
1.9%
7 53
2.4%
8 46
2.1%
9 38
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

Fish
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.538633
Minimum0
Maximum259
Zeros384
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:17.872373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.1
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.637617
Coefficient of variation (CV)1.4555036
Kurtosis3.0934393
Mean37.538633
Median Absolute Deviation (MAD)12
Skewness1.9190627
Sum84049
Variance2985.2692
MonotonicityNot monotonic
2023-07-31T17:57:18.282114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
 
17.2%
2 156
 
7.0%
3 130
 
5.8%
4 108
 
4.8%
6 82
 
3.7%
7 66
 
2.9%
8 57
 
2.5%
10 55
 
2.5%
13 48
 
2.1%
12 47
 
2.1%
Other values (172) 1106
49.4%
ValueCountFrequency (%)
0 384
17.2%
1 10
 
0.4%
2 156
7.0%
3 130
 
5.8%
4 108
 
4.8%
5 1
 
< 0.1%
6 82
 
3.7%
7 66
 
2.9%
8 57
 
2.5%
10 55
 
2.5%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

Sweet
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.074587
Minimum0
Maximum263
Zeros419
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:18.688486image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile126
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.286043
Coefficient of variation (CV)1.5249002
Kurtosis4.3734034
Mean27.074587
Median Absolute Deviation (MAD)8
Skewness2.1354411
Sum60620
Variance1704.5373
MonotonicityNot monotonic
2023-07-31T17:57:19.014611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 419
 
18.7%
1 160
 
7.1%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
12 45
 
2.0%
Other values (167) 1062
47.4%
ValueCountFrequency (%)
0 419
18.7%
1 160
 
7.1%
2 128
 
5.7%
3 101
 
4.5%
4 82
 
3.7%
5 65
 
2.9%
6 64
 
2.9%
7 57
 
2.5%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

Gold
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct213
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.036177
Minimum0
Maximum362
Zeros61
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:19.354298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q356
95-th percentile165.1
Maximum362
Range362
Interquartile range (IQR)47

Descriptive statistics

Standard deviation52.1747
Coefficient of variation (CV)1.1848145
Kurtosis3.5488593
Mean44.036177
Median Absolute Deviation (MAD)18
Skewness1.8854417
Sum98597
Variance2722.1993
MonotonicityNot monotonic
2023-07-31T17:57:19.653477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73
 
3.3%
4 70
 
3.1%
3 69
 
3.1%
5 63
 
2.8%
2 62
 
2.8%
12 62
 
2.8%
0 61
 
2.7%
6 57
 
2.5%
7 54
 
2.4%
10 49
 
2.2%
Other values (203) 1619
72.3%
ValueCountFrequency (%)
0 61
2.7%
1 73
3.3%
2 62
2.8%
3 69
3.1%
4 70
3.1%
5 63
2.8%
6 57
2.5%
7 54
2.4%
8 40
1.8%
9 44
2.0%
ValueCountFrequency (%)
362 1
< 0.1%
321 1
< 0.1%
291 1
< 0.1%
262 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
246 1
< 0.1%
245 1
< 0.1%
242 2
0.1%

Deals
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3242519
Minimum0
Maximum15
Zeros46
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:19.912964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9323446
Coefficient of variation (CV)0.83138348
Kurtosis8.9431137
Mean2.3242519
Median Absolute Deviation (MAD)1
Skewness2.4201198
Sum5204
Variance3.7339558
MonotonicityNot monotonic
2023-07-31T17:57:20.151139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 188
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
0 46
 
2.1%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 24
 
1.1%
ValueCountFrequency (%)
0 46
 
2.1%
1 970
43.3%
2 497
22.2%
3 297
 
13.3%
4 188
 
8.4%
5 94
 
4.2%
6 61
 
2.7%
7 40
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 4
 
0.2%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 40
1.8%
6 61
2.7%
5 94
4.2%

Web
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0853059
Minimum0
Maximum27
Zeros49
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:20.409102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7792402
Coefficient of variation (CV)0.68030162
Kurtosis5.6994576
Mean4.0853059
Median Absolute Deviation (MAD)2
Skewness1.3821304
Sum9147
Variance7.7241763
MonotonicityNot monotonic
2023-07-31T17:57:20.651948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 373
16.7%
1 354
15.8%
3 335
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
0 49
 
2.2%
Other values (5) 91
 
4.1%
ValueCountFrequency (%)
0 49
 
2.2%
1 354
15.8%
2 373
16.7%
3 335
15.0%
4 280
12.5%
5 220
9.8%
6 205
9.2%
7 155
6.9%
8 102
 
4.6%
9 75
 
3.3%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.3%
8 102
4.6%
7 155
6.9%
6 205
9.2%
5 220
9.8%

Catalog
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6627959
Minimum0
Maximum28
Zeros586
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:20.847600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9235425
Coefficient of variation (CV)1.0979221
Kurtosis8.0437442
Mean2.6627959
Median Absolute Deviation (MAD)2
Skewness1.8802968
Sum5962
Variance8.5471005
MonotonicityNot monotonic
2023-07-31T17:57:21.064173image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 586
26.2%
1 496
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.3%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
10 48
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 586
26.2%
1 496
22.2%
2 276
12.3%
3 184
 
8.2%
4 182
 
8.1%
5 140
 
6.3%
6 128
 
5.7%
7 79
 
3.5%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.8%
10 48
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.5%
6 128
5.7%
5 140
6.3%
4 182
8.1%

Store
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7914247
Minimum0
Maximum13
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:21.292196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2511492
Coefficient of variation (CV)0.56137295
Kurtosis-0.62286449
Mean5.7914247
Median Absolute Deviation (MAD)2
Skewness0.70155925
Sum12967
Variance10.569971
MonotonicityNot monotonic
2023-07-31T17:57:21.513758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 489
21.8%
4 323
14.4%
2 223
10.0%
5 212
9.5%
6 178
 
7.9%
8 149
 
6.7%
7 143
 
6.4%
10 125
 
5.6%
9 106
 
4.7%
12 105
 
4.7%
Other values (4) 186
 
8.3%
ValueCountFrequency (%)
0 15
 
0.7%
1 7
 
0.3%
2 223
10.0%
3 489
21.8%
4 323
14.4%
5 212
9.5%
6 178
 
7.9%
7 143
 
6.4%
8 149
 
6.7%
9 106
 
4.7%
ValueCountFrequency (%)
13 83
 
3.7%
12 105
 
4.7%
11 81
 
3.6%
10 125
 
5.6%
9 106
 
4.7%
8 149
6.7%
7 143
6.4%
6 178
7.9%
5 212
9.5%
4 323
14.4%

Web Visit
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3162126
Minimum0
Maximum20
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:21.750348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4271441
Coefficient of variation (CV)0.45655512
Kurtosis1.8199066
Mean5.3162126
Median Absolute Deviation (MAD)2
Skewness0.20825792
Sum11903
Variance5.8910284
MonotonicityNot monotonic
2023-07-31T17:57:22.006860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 393
17.6%
8 342
15.3%
6 339
15.1%
5 281
12.6%
4 218
9.7%
3 205
9.2%
2 202
9.0%
1 153
 
6.8%
9 83
 
3.7%
0 11
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 11
 
0.5%
1 153
 
6.8%
2 202
9.0%
3 205
9.2%
4 218
9.7%
5 281
12.6%
6 339
15.1%
7 393
17.6%
8 342
15.3%
9 83
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 83
 
3.7%
8 342
15.3%
7 393
17.6%
6 339
15.1%

AcceptedCmp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2076 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Length

2023-07-31T17:57:22.403799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:22.720949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

AcceptedCmp4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2072 
1
 
167

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

Length

2023-07-31T17:57:22.934248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:23.162620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2072
92.5%
1 167
 
7.5%

AcceptedCmp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2076 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Length

2023-07-31T17:57:23.361041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:23.634868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2076
92.7%
1 163
 
7.3%

AcceptedCmp1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2095 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

Length

2023-07-31T17:57:23.822218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:24.055238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2095
93.6%
1 144
 
6.4%

AcceptedCmp2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2209 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Length

2023-07-31T17:57:24.246429image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:24.516716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2209
98.7%
1 30
 
1.3%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
2218 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Length

2023-07-31T17:57:24.710688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:24.958701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2218
99.1%
1 21
 
0.9%

Z_CostContact
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
3
2239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2239
100.0%

Length

2023-07-31T17:57:25.160977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:25.668619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2239
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2239
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2239
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2239
100.0%

Z_Revenue
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
11
2239 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4478
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2239
100.0%

Length

2023-07-31T17:57:26.128520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:26.626785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
11 2239
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4478
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4478
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4478
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4478
100.0%

Response
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0
1905 
1
334 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

Length

2023-07-31T17:57:26.935958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:27.345994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1905
85.1%
1 334
 
14.9%

lifespan_months
Real number (ℝ)

Distinct24
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.541313
Minimum3
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:27.686284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q19
median15
Q320
95-th percentile25
Maximum26
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7536747
Coefficient of variation (CV)0.46444737
Kurtosis-1.1874761
Mean14.541313
Median Absolute Deviation (MAD)6
Skewness-0.011931994
Sum32558
Variance45.612122
MonotonicityNot monotonic
2023-07-31T17:57:28.011910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
25 123
 
5.5%
11 111
 
5.0%
19 110
 
4.9%
12 109
 
4.9%
23 105
 
4.7%
17 103
 
4.6%
6 103
 
4.6%
4 98
 
4.4%
15 97
 
4.3%
21 96
 
4.3%
Other values (14) 1184
52.9%
ValueCountFrequency (%)
3 65
2.9%
4 98
4.4%
5 92
4.1%
6 103
4.6%
7 96
4.3%
8 86
3.8%
9 85
3.8%
10 83
3.7%
11 111
5.0%
12 109
4.9%
ValueCountFrequency (%)
26 51
2.3%
25 123
5.5%
24 89
4.0%
23 105
4.7%
22 81
3.6%
21 96
4.3%
20 85
3.8%
19 110
4.9%
18 96
4.3%
17 103
4.6%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.100491
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:28.545883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q137
median44
Q355
95-th percentile64
Maximum74
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.695504
Coefficient of variation (CV)0.25932098
Kurtosis-0.79313601
Mean45.100491
Median Absolute Deviation (MAD)9
Skewness0.092932825
Sum100980
Variance136.7848
MonotonicityNot monotonic
2023-07-31T17:57:28.917359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 89
 
4.0%
43 87
 
3.9%
39 83
 
3.7%
44 80
 
3.6%
42 79
 
3.5%
36 77
 
3.4%
49 74
 
3.3%
41 74
 
3.3%
45 71
 
3.2%
40 69
 
3.1%
Other values (46) 1456
65.0%
ValueCountFrequency (%)
18 2
 
0.1%
19 5
 
0.2%
20 3
 
0.1%
21 5
 
0.2%
22 13
0.6%
23 15
0.7%
24 18
0.8%
25 30
1.3%
26 29
1.3%
27 27
1.2%
ValueCountFrequency (%)
74 1
 
< 0.1%
73 1
 
< 0.1%
71 7
 
0.3%
70 7
 
0.3%
69 8
 
0.4%
68 16
0.7%
67 16
0.7%
66 21
0.9%
65 30
1.3%
64 29
1.3%

Age group
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.9 KiB
Senior
1073 
Adult
945 
Young-adult
221 

Length

Max length11
Median length6
Mean length6.0714605
Min length5

Characters and Unicode

Total characters13594
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSenior
2nd rowSenior
3rd rowSenior
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Senior 1073
47.9%
Adult 945
42.2%
Young-adult 221
 
9.9%

Length

2023-07-31T17:57:29.182042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:29.457418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
senior 1073
47.9%
adult 945
42.2%
young-adult 221
 
9.9%

Most occurring characters

ValueCountFrequency (%)
u 1387
10.2%
n 1294
9.5%
o 1294
9.5%
d 1166
8.6%
l 1166
8.6%
t 1166
8.6%
S 1073
7.9%
e 1073
7.9%
i 1073
7.9%
r 1073
7.9%
Other values (5) 1829
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11134
81.9%
Uppercase Letter 2239
 
16.5%
Dash Punctuation 221
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 1387
12.5%
n 1294
11.6%
o 1294
11.6%
d 1166
10.5%
l 1166
10.5%
t 1166
10.5%
e 1073
9.6%
i 1073
9.6%
r 1073
9.6%
g 221
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
S 1073
47.9%
A 945
42.2%
Y 221
 
9.9%
Dash Punctuation
ValueCountFrequency (%)
- 221
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13373
98.4%
Common 221
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 1387
10.4%
n 1294
9.7%
o 1294
9.7%
d 1166
8.7%
l 1166
8.7%
t 1166
8.7%
S 1073
8.0%
e 1073
8.0%
i 1073
8.0%
r 1073
8.0%
Other values (4) 1608
12.0%
Common
ValueCountFrequency (%)
- 221
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 1387
10.2%
n 1294
9.5%
o 1294
9.5%
d 1166
8.6%
l 1166
8.6%
t 1166
8.6%
S 1073
7.9%
e 1073
7.9%
i 1073
7.9%
r 1073
7.9%
Other values (5) 1829
13.5%

Education_clean
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Postgraduate
1982 
Undergraduate
257 

Length

Max length13
Median length12
Mean length12.114783
Min length12

Characters and Unicode

Total characters27125
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPostgraduate
2nd rowPostgraduate
3rd rowPostgraduate
4th rowPostgraduate
5th rowPostgraduate

Common Values

ValueCountFrequency (%)
Postgraduate 1982
88.5%
Undergraduate 257
 
11.5%

Length

2023-07-31T17:57:29.685253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:30.200059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
postgraduate 1982
88.5%
undergraduate 257
 
11.5%

Most occurring characters

ValueCountFrequency (%)
a 4478
16.5%
t 4221
15.6%
r 2496
9.2%
d 2496
9.2%
e 2496
9.2%
g 2239
8.3%
u 2239
8.3%
P 1982
7.3%
o 1982
7.3%
s 1982
7.3%
Other values (2) 514
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24886
91.7%
Uppercase Letter 2239
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4478
18.0%
t 4221
17.0%
r 2496
10.0%
d 2496
10.0%
e 2496
10.0%
g 2239
9.0%
u 2239
9.0%
o 1982
8.0%
s 1982
8.0%
n 257
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P 1982
88.5%
U 257
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 27125
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4478
16.5%
t 4221
15.6%
r 2496
9.2%
d 2496
9.2%
e 2496
9.2%
g 2239
8.3%
u 2239
8.3%
P 1982
7.3%
o 1982
7.3%
s 1982
7.3%
Other values (2) 514
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4478
16.5%
t 4221
15.6%
r 2496
9.2%
d 2496
9.2%
e 2496
9.2%
g 2239
8.3%
u 2239
8.3%
P 1982
7.3%
o 1982
7.3%
s 1982
7.3%
Other values (2) 514
 
1.9%

Marital_clean
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Couple
1443 
Single
796 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13434
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowCouple
4th rowCouple
5th rowCouple

Common Values

ValueCountFrequency (%)
Couple 1443
64.4%
Single 796
35.6%

Length

2023-07-31T17:57:30.491679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:30.826783image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
couple 1443
64.4%
single 796
35.6%

Most occurring characters

ValueCountFrequency (%)
l 2239
16.7%
e 2239
16.7%
C 1443
10.7%
o 1443
10.7%
u 1443
10.7%
p 1443
10.7%
S 796
 
5.9%
i 796
 
5.9%
n 796
 
5.9%
g 796
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11195
83.3%
Uppercase Letter 2239
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2239
20.0%
e 2239
20.0%
o 1443
12.9%
u 1443
12.9%
p 1443
12.9%
i 796
 
7.1%
n 796
 
7.1%
g 796
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 1443
64.4%
S 796
35.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 13434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2239
16.7%
e 2239
16.7%
C 1443
10.7%
o 1443
10.7%
u 1443
10.7%
p 1443
10.7%
S 796
 
5.9%
i 796
 
5.9%
n 796
 
5.9%
g 796
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2239
16.7%
e 2239
16.7%
C 1443
10.7%
o 1443
10.7%
u 1443
10.7%
p 1443
10.7%
S 796
 
5.9%
i 796
 
5.9%
n 796
 
5.9%
g 796
 
5.9%

Children
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
1
1127 
0
638 
2
421 
3
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Length

2023-07-31T17:57:31.180274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:31.465243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1127
50.3%
0 638
28.5%
2 421
 
18.8%
3 53
 
2.4%

Family_size
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
3
976 
2
695 
4
364 
1
167 
5
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2239
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Length

2023-07-31T17:57:31.741467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:32.027725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Most occurring characters

ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 976
43.6%
2 695
31.0%
4 364
 
16.3%
1 167
 
7.5%
5 37
 
1.7%

Promotion accepted
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44707459
Minimum0
Maximum5
Zeros1630
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:32.229562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8906922
Coefficient of variation (CV)1.9922676
Kurtosis6.3393441
Mean0.44707459
Median Absolute Deviation (MAD)0
Skewness2.4402721
Sum1001
Variance0.79333259
MonotonicityNot monotonic
2023-07-31T17:57:32.431998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1630
72.8%
1 370
 
16.5%
2 142
 
6.3%
3 51
 
2.3%
4 36
 
1.6%
5 10
 
0.4%
ValueCountFrequency (%)
0 1630
72.8%
1 370
 
16.5%
2 142
 
6.3%
3 51
 
2.3%
4 36
 
1.6%
5 10
 
0.4%
ValueCountFrequency (%)
5 10
 
0.4%
4 36
 
1.6%
3 51
 
2.3%
2 142
 
6.3%
1 370
 
16.5%
0 1630
72.8%

PromoAccepted
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44707459
Minimum0
Maximum5
Zeros1630
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:32.698853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8906922
Coefficient of variation (CV)1.9922676
Kurtosis6.3393441
Mean0.44707459
Median Absolute Deviation (MAD)0
Skewness2.4402721
Sum1001
Variance0.79333259
MonotonicityNot monotonic
2023-07-31T17:57:32.941609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1630
72.8%
1 370
 
16.5%
2 142
 
6.3%
3 51
 
2.3%
4 36
 
1.6%
5 10
 
0.4%
ValueCountFrequency (%)
0 1630
72.8%
1 370
 
16.5%
2 142
 
6.3%
3 51
 
2.3%
4 36
 
1.6%
5 10
 
0.4%
ValueCountFrequency (%)
5 10
 
0.4%
4 36
 
1.6%
3 51
 
2.3%
2 142
 
6.3%
1 370
 
16.5%
0 1630
72.8%

Responder
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
None
1630 
Low
563 
High
 
46

Length

Max length4
Median length4
Mean length3.7485485
Min length3

Characters and Unicode

Total characters8393
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 1630
72.8%
Low 563
 
25.1%
High 46
 
2.1%

Length

2023-07-31T17:57:33.174495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-31T17:57:33.602593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
none 1630
72.8%
low 563
 
25.1%
high 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
o 2193
26.1%
N 1630
19.4%
n 1630
19.4%
e 1630
19.4%
L 563
 
6.7%
w 563
 
6.7%
H 46
 
0.5%
i 46
 
0.5%
g 46
 
0.5%
h 46
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6154
73.3%
Uppercase Letter 2239
 
26.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2193
35.6%
n 1630
26.5%
e 1630
26.5%
w 563
 
9.1%
i 46
 
0.7%
g 46
 
0.7%
h 46
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N 1630
72.8%
L 563
 
25.1%
H 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8393
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2193
26.1%
N 1630
19.4%
n 1630
19.4%
e 1630
19.4%
L 563
 
6.7%
w 563
 
6.7%
H 46
 
0.5%
i 46
 
0.5%
g 46
 
0.5%
h 46
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2193
26.1%
N 1630
19.4%
n 1630
19.4%
e 1630
19.4%
L 563
 
6.7%
w 563
 
6.7%
H 46
 
0.5%
i 46
 
0.5%
g 46
 
0.5%
h 46
 
0.5%

Frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.539527
Minimum0
Maximum32
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:33.820012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median12
Q318
95-th percentile24
Maximum32
Range32
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2063996
Coefficient of variation (CV)0.57469471
Kurtosis-1.1183696
Mean12.539527
Median Absolute Deviation (MAD)6
Skewness0.29633737
Sum28076
Variance51.932195
MonotonicityNot monotonic
2023-07-31T17:57:34.113944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 202
 
9.0%
6 191
 
8.5%
5 180
 
8.0%
3 128
 
5.7%
7 127
 
5.7%
18 104
 
4.6%
14 100
 
4.5%
16 98
 
4.4%
17 89
 
4.0%
21 86
 
3.8%
Other values (23) 934
41.7%
ValueCountFrequency (%)
0 6
 
0.3%
1 6
 
0.3%
2 2
 
0.1%
3 128
5.7%
4 202
9.0%
5 180
8.0%
6 191
8.5%
7 127
5.7%
8 51
 
2.3%
9 45
 
2.0%
ValueCountFrequency (%)
32 3
 
0.1%
31 2
 
0.1%
30 2
 
0.1%
29 6
 
0.3%
28 10
 
0.4%
27 23
 
1.0%
26 24
 
1.1%
25 39
1.7%
24 52
2.3%
23 64
2.9%

Monetary
Real number (ℝ)

HIGH CORRELATION 

Distinct1054
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.04109
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 KiB
2023-07-31T17:57:34.396278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median396
Q31046
95-th percentile1772.6
Maximum2525
Range2520
Interquartile range (IQR)977

Descriptive statistics

Standard deviation602.27409
Coefficient of variation (CV)0.99378425
Kurtosis-0.34287343
Mean606.04109
Median Absolute Deviation (MAD)353
Skewness0.86023744
Sum1356926
Variance362734.08
MonotonicityNot monotonic
2023-07-31T17:57:34.701365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 19
 
0.8%
22 18
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
48 14
 
0.6%
20 14
 
0.6%
43 14
 
0.6%
37 14
 
0.6%
38 14
 
0.6%
Other values (1044) 2086
93.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
8 4
 
0.2%
9 2
 
0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.4%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

Interactions

2023-07-31T17:56:51.995871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:49.476516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:55.913099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:03.340779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:14.198353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:20.371173image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:26.168752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:32.098161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:45.784382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:56.270761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:11.645351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:22.761160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:30.670313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:41.361318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:48.709700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:54.214828image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:01.739993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:07.370905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:14.786136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:25.718596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:39.075885image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:52.559379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:49.890316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:56.226528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:03.763647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:14.813837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:20.637696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:26.417862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:32.375960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:46.057644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:56.663342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:12.292925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:23.185027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:31.016354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:41.996619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:49.046232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:54.500481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:01.996666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:07.614717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:15.527141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:26.498298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:40.111487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:53.056556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:50.324487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:56.518102image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:04.059990image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:15.268872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:20.889096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:26.686772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-31T17:56:32.628074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:48.640703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:57.285410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:53.532254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:59.531500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:09.513339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:18.139503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:23.542239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:29.703488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:39.378908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:51.264693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:06.305900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:18.826252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:27.763754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:36.052155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:46.139382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:51.951550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:57.559054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:04.981504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:10.474403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:22.320964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:33.549147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:48.990996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:57.549670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:53.787286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:59.784193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:10.201729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:18.416257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:23.775443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:29.945552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:40.297498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:52.001153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:07.321184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:19.387751image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:28.218174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:36.777772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:46.371525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:52.200332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:57.856117image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:05.214566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:11.648466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:22.714910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:34.148013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:49.313171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:57.774235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:54.026686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:00.223531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:10.770208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:18.667646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:24.138474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:30.206487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:40.764651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:52.567974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:08.055220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:19.896881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:28.525488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:37.345112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:46.606947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:52.452168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:58.420534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:05.470426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:12.100184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:23.019955image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:34.510081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:49.692607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:58.126821image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:54.300550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:00.694271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:11.328026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:18.931697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:24.508969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:30.475453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:41.636352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:52.968935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:08.802559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:20.435066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:28.798635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:37.816368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:46.861235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:52.733543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:59.109180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:05.773234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:12.405528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:23.297181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:34.921942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:50.109455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:58.564651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:54.510452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:00.981036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:11.890132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:19.145007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:24.757263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:30.749150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:42.272168image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:53.289800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:09.316400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:20.919286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:29.029725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:38.529519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:47.116637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:52.980864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:59.865193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:06.005721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:12.713897image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:23.644254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:35.215306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:50.420944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:58.999488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:54.755846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:01.445054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:12.347188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:19.405566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:24.997225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:31.029911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:43.283473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:53.883262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:09.856190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:21.274334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:29.299417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:39.180329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:47.437849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:53.240647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:00.364131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:06.270079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:13.067099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:24.041189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:35.486579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:50.747238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:59.377136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:54.980254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:02.360537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:12.826645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:19.624016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:25.420638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:31.288260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:43.845914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:54.627979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:10.380081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:21.610028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:29.596818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:39.838104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:47.734304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:53.477755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:00.822419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:06.514576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:13.378301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:24.399749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:35.854258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:51.049734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:59.721388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:55.191323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:02.743073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:13.098950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:19.851563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:25.678641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:31.551258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:44.724148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:55.243498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:10.679949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:21.977566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:29.966188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:40.326081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:48.064420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:53.733043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:01.125641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:06.804800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:13.609800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:24.667511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:36.436904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:51.298013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:57:00.184150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:53:55.472536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:03.021361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:13.559712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:20.128822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:25.933583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:31.831049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:45.480159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:54:55.696286image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:11.049945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:22.344276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:30.348141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:40.834982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:48.398526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:55:53.984406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:01.436774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:07.138456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:13.857373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:25.176963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:37.388584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-31T17:56:51.584491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-31T17:57:35.145199image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
IDYear_BirthIncomeRecencyWinesFruitsMeatFishSweetGoldDealsWebCatalogStoreWeb Visitlifespan_monthsAgePromotion acceptedPromoAcceptedFrequencyMonetaryEducationMarital_StatusKidhomeTeenhomeAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseAge groupEducation_cleanMarital_cleanChildrenFamily_sizeResponder
ID1.0000.0030.003-0.046-0.025-0.022-0.013-0.029-0.034-0.041-0.025-0.025-0.012-0.023-0.0110.001-0.003-0.036-0.036-0.017-0.0250.0000.0050.0000.0000.0000.0000.0000.0450.0350.0000.0310.0000.0000.0000.0000.0000.000
Year_Birth0.0031.000-0.216-0.020-0.235-0.026-0.114-0.0300.003-0.076-0.090-0.166-0.179-0.1700.1290.016-1.000-0.021-0.021-0.179-0.1580.1120.1100.2310.3410.0570.0510.1010.0610.0080.0000.0700.9690.1490.0930.2160.1860.058
Income0.003-0.2161.0000.0090.8290.5790.8140.5750.5650.504-0.1940.5720.7890.730-0.641-0.0210.2160.3100.3100.7760.8490.1820.0000.3990.3260.0670.2640.5620.3970.1440.0000.2570.2000.2350.0320.3250.2280.304
Recency-0.046-0.0200.0091.0000.0180.0250.0280.0130.0240.0180.008-0.0040.0310.005-0.0220.0240.020-0.103-0.1030.0110.0200.0000.0260.0700.0500.0440.0000.0000.0000.0340.0000.2080.0500.0000.0470.0380.0340.071
Wines-0.025-0.2350.8290.0181.0000.5180.8240.5250.5050.5750.0580.7400.8240.807-0.3890.1560.2350.3840.3840.8680.9270.1140.0190.4060.1170.0950.3950.5170.3580.3020.0000.2680.1370.1520.0390.2170.1500.367
Fruits-0.022-0.0260.5790.0250.5181.0000.7130.7050.6920.569-0.1100.4710.6350.584-0.4430.1310.0260.1750.1750.6270.6830.0700.0290.3120.1210.0040.0720.2850.2610.0000.0000.1520.0620.0320.0000.2670.1840.170
Meat-0.013-0.1140.8140.0280.8240.7131.0000.7260.6960.638-0.0320.6790.8520.779-0.4920.1580.1140.3120.3120.8610.9390.0540.0300.3220.2270.0290.0990.3780.3100.0340.0000.2430.1010.0720.0640.3490.2530.234
Fish-0.029-0.0300.5750.0130.5250.7050.7261.0000.7010.565-0.1200.4660.6570.583-0.4580.1340.0300.1530.1530.6330.6960.0620.0530.3230.1390.0770.0000.2670.2700.0470.0000.1310.0710.0000.0000.2850.2040.136
Sweet-0.0340.0030.5650.0240.5050.6920.6960.7011.0000.543-0.1060.4640.6280.581-0.4490.119-0.0030.1690.1690.6270.6700.0680.0000.2910.1010.0000.0280.2680.2590.0460.0000.1130.0460.0470.0420.2460.1740.142
Gold-0.041-0.0760.5040.0180.5750.5690.6380.5650.5431.0000.0900.5800.6490.540-0.2610.2260.0760.2530.2530.6470.6920.0660.0540.2640.0560.1260.0470.1770.1640.0320.0000.1380.0740.0000.0650.1620.1270.135
Deals-0.025-0.090-0.1940.0080.058-0.110-0.032-0.120-0.1060.0901.0000.284-0.0400.1000.3980.2150.090-0.092-0.0920.109-0.0140.0000.0190.2100.3470.0000.0510.2440.1640.0000.0000.0960.0920.0000.0220.3680.2850.104
Web-0.025-0.1660.572-0.0040.7400.4710.6790.4660.4640.5800.2841.0000.6190.673-0.0970.2020.1660.2540.2540.8340.7270.0830.0390.2940.1600.0240.1550.1710.1650.0000.0000.1660.1070.1180.0120.1490.1080.164
Catalog-0.012-0.1790.7890.0310.8240.6350.8520.6570.6280.649-0.0400.6191.0000.709-0.5360.1260.1790.3660.3660.8700.8920.0650.0000.3860.1190.0890.1920.3590.3140.1110.0000.2190.1000.0970.0220.2920.2040.245
Store-0.023-0.1700.7300.0050.8070.5840.7790.5830.5810.5400.1000.6730.7091.000-0.4540.1150.1700.1740.1740.8850.8050.1040.0250.4030.0850.1780.2120.2290.1970.0810.0000.1480.1280.1400.0000.2000.1350.171
Web Visit-0.0110.129-0.641-0.022-0.389-0.443-0.492-0.458-0.449-0.2610.398-0.097-0.536-0.4541.0000.298-0.129-0.078-0.078-0.420-0.4760.0540.0000.3450.2180.0770.0000.3090.2020.0000.0000.1200.0960.0610.0270.3250.2280.111
lifespan_months0.0010.016-0.0210.0240.1560.1310.1580.1340.1190.2260.2150.2020.1260.1150.2981.000-0.0160.0850.0850.1610.1870.0550.0380.0000.0000.0280.0210.0000.0000.0000.0650.1990.0150.0500.0000.0180.0200.078
Age-0.003-1.0000.2160.0200.2350.0260.1140.030-0.0030.0760.0900.1660.1790.170-0.129-0.0161.0000.0210.0210.1790.1580.1120.1080.2330.3380.0560.0550.1000.0600.0340.0000.0670.9530.1490.0900.2150.1860.058
Promotion accepted-0.036-0.0210.310-0.1030.3840.1750.3120.1530.1690.253-0.0920.2540.3660.174-0.0780.0850.0211.0001.0000.2880.3890.0230.0350.1340.1150.5040.5450.7010.6670.6320.0000.7450.0540.0480.0520.1690.1260.999
PromoAccepted-0.036-0.0210.310-0.1030.3840.1750.3120.1530.1690.253-0.0920.2540.3660.174-0.0780.0850.0211.0001.0000.2880.3890.0230.0350.1340.1150.5040.5450.7010.6670.6320.0000.7450.0540.0480.0520.1690.1260.999
Frequency-0.017-0.1790.7760.0110.8680.6270.8610.6330.6270.6470.1090.8340.8700.885-0.4200.1610.1790.2880.2881.0000.9080.1020.0000.4350.0790.0660.2200.2870.2700.1020.0190.1650.1310.1530.0000.2320.1610.212
Monetary-0.025-0.1580.8490.0200.9270.6830.9390.6960.6700.692-0.0140.7270.8920.805-0.4760.1870.1580.3890.3890.9081.0000.0910.0230.4370.2260.0560.2510.5260.4170.1510.0000.2940.1350.1350.0000.3240.2260.347
Education0.0000.1120.1820.0000.1140.0700.0540.0620.0680.0660.0000.0830.0650.1040.0540.0550.1120.0230.0230.1020.0911.0000.0000.0510.1040.0000.0480.0340.0350.0170.0390.0920.1340.9990.0000.0330.0430.045
Marital_Status0.0050.1100.0000.0260.0190.0290.0300.0530.0000.0540.0190.0390.0000.0250.0000.0380.1080.0350.0350.0000.0230.0001.0000.0400.0740.0000.0000.0270.0310.0000.0000.1450.1540.0000.9990.0450.3060.000
Kidhome0.0000.2310.3990.0700.4060.3120.3220.3230.2910.2640.2100.2940.3860.4030.3450.0000.2330.1340.1340.4350.4370.0510.0401.0000.0540.0300.1620.2100.1820.0790.0270.0750.2050.0580.0000.6250.5450.125
Teenhome0.0000.3410.3260.0500.1170.1210.2270.1390.1010.0560.3470.1600.1190.0850.2180.0000.3380.1150.1150.0790.2260.1040.0740.0541.0000.0380.0260.2050.1450.0000.0000.1590.2930.1040.0100.5640.5120.109
AcceptedCmp30.0000.0570.0670.0440.0950.0040.0290.0770.0000.1260.0000.0240.0890.1780.0770.0280.0560.5040.5040.0660.0560.0000.0000.0300.0381.0000.0730.0740.0890.0610.0000.2510.0550.0000.0000.0000.0150.459
AcceptedCmp40.0000.0510.2640.0000.3950.0720.0990.0000.0280.0470.0510.1550.1920.2120.0000.0210.0550.5450.5450.2200.2510.0480.0000.1620.0260.0731.0000.3030.2470.2840.0000.1730.0620.0410.0000.0850.0560.516
AcceptedCmp50.0000.1010.5620.0000.5170.2850.3780.2670.2680.1770.2440.1710.3590.2290.3090.0000.1000.7010.7010.2870.5260.0340.0270.2100.2050.0740.3031.0000.3990.2130.0000.3240.0560.0390.0000.3470.2700.605
AcceptedCmp10.0450.0610.3970.0000.3580.2610.3100.2700.2590.1640.1640.1650.3140.1970.2020.0000.0600.6670.6670.2700.4170.0350.0310.1820.1450.0890.2470.3991.0000.1660.0000.2910.0530.0000.0000.2780.2150.594
AcceptedCmp20.0350.0080.1440.0340.3020.0000.0340.0470.0460.0320.0000.0000.1110.0810.0000.0000.0340.6320.6320.1020.1510.0170.0000.0790.0000.0610.2840.2130.1661.0000.0000.1620.0000.0000.0000.0730.0430.456
Complain0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0190.0000.0390.0000.0270.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
Response0.0310.0700.2570.2080.2680.1520.2430.1310.1130.1380.0960.1660.2190.1480.1200.1990.0670.7450.7450.1650.2940.0920.1450.0750.1590.2510.1730.3240.2910.1620.0001.0000.0060.0500.1470.2040.2220.708
Age group0.0000.9690.2000.0500.1370.0620.1010.0710.0460.0740.0920.1070.1000.1280.0960.0150.9530.0540.0540.1310.1350.1340.1540.2050.2930.0550.0620.0560.0530.0000.0000.0061.0000.1430.0740.1890.1990.000
Education_clean0.0000.1490.2350.0000.1520.0320.0720.0000.0470.0000.0000.1180.0970.1400.0610.0500.1490.0480.0480.1530.1350.9990.0000.0580.1040.0000.0410.0390.0000.0000.0000.0500.1431.0000.0000.0370.0590.045
Marital_clean0.0000.0930.0320.0470.0390.0000.0640.0000.0420.0650.0220.0120.0220.0000.0270.0000.0900.0520.0520.0000.0000.0000.9990.0000.0100.0000.0000.0000.0000.0000.0000.1470.0740.0001.0000.0470.4330.057
Children0.0000.2160.3250.0380.2170.2670.3490.2850.2460.1620.3680.1490.2920.2000.3250.0180.2150.1690.1690.2320.3240.0330.0450.6250.5640.0000.0850.3470.2780.0730.0000.2040.1890.0370.0471.0000.8330.192
Family_size0.0000.1860.2280.0340.1500.1840.2530.2040.1740.1270.2850.1080.2040.1350.2280.0200.1860.1260.1260.1610.2260.0430.3060.5450.5120.0150.0560.2700.2150.0430.0000.2220.1990.0590.4330.8331.0000.163
Responder0.0000.0580.3040.0710.3670.1700.2340.1360.1420.1350.1040.1640.2450.1710.1110.0780.0580.9990.9990.2120.3470.0450.0000.1250.1090.4590.5160.6050.5940.4560.0000.7080.0000.0450.0570.1920.1631.000

Missing values

2023-07-31T17:57:00.855943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-31T17:57:02.628917image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeEnrollment dateRecencyWinesFruitsMeatFishSweetGoldDealsWebCatalogStoreWeb VisitAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponselifespan_monthsAgeAge groupEducation_cleanMarital_cleanChildrenFamily_sizePromotion acceptedPromoAcceptedResponderFrequencyMonetary
055241957GraduationSingle58138.0002012-09-045863588546172888838104700000031112557SeniorPostgraduateSingle0111Low221617
121741954GraduationSingle46344.0112014-03-08381116216211250000003110760SeniorPostgraduateSingle2300None427
241411965GraduationTogether71613.0002013-08-212642649127111214218210400000031101349SeniorPostgraduateCouple0200None20776
361821984GraduationTogether26646.0102014-02-1026114201035220460000003110730AdultPostgraduateCouple1300None653
453241981PhDMarried58293.0102014-01-199417343118462715553650000003110833AdultPostgraduateCouple1300None14422
574461967MasterTogether62513.0012013-09-091652042980421426410600000031101347SeniorPostgraduateCouple1300None20716
69651971GraduationDivorced55635.0012012-11-1334235651645049274737600000031102343AdultPostgraduateSingle1300None17590
761771985PhDMarried33454.0102013-05-083276105631232404800000031101729Young-adultPostgraduateCouple1300None8169
848551974PhDTogether30351.0102013-06-0619140243321302900000031111640AdultPostgraduateCouple1311Low546
958991950PhDTogether5648.0112014-03-1368280611131100201000003110664SeniorPostgraduateCouple2411Low149
IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeEnrollment dateRecencyWinesFruitsMeatFishSweetGoldDealsWebCatalogStoreWeb VisitAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponselifespan_monthsAgeAge groupEducation_cleanMarital_cleanChildrenFamily_sizePromotion acceptedPromoAcceptedResponderFrequencyMonetary
2229100841972GraduationMarried24434.0202014-05-18932820017221270000003110442AdultPostgraduateCouple2400None550
223070041984GraduationSingle11012.0102013-03-16822432671233312910000031101830AdultPostgraduateSingle1211Low684
223198171970MasterSingle44802.0002012-08-21718531014313102029412800000031102544AdultPostgraduateSingle0100None251049
223280801986GraduationSingle26816.0002012-08-17505163431003400000031102528Young-adultPostgraduateSingle0100None322
223483721974GraduationMarried34421.0102013-07-01813376291102700000031101540AdultPostgraduateCouple1300None330
2235108701967GraduationMarried61223.0012013-06-134670943182421182472934500000031101547SeniorPostgraduateCouple1300None161341
223640011946PhDTogether64014.0212014-06-1056406030008782570001003110368SeniorPostgraduateCouple3511Low15444
223772701981GraduationDivorced56981.0002014-01-2591908482173212241231360100003110833AdultPostgraduateSingle0211Low181241
223882351956MasterTogether69245.0012014-01-248428302148030612651030000003110858SeniorPostgraduateCouple1300None21843
223994051954PhDMarried52869.0112012-10-15408436121213314700000031112360SeniorPostgraduateCouple2411Low8172